A New Method of Classroom Behavior Recognition Based on WS-FC SLOWFAST

With the growing integration of deep learning and educational informatization, applying artificial intelligence to classroom behavior analysis has garnered significant attention. This article specifies 14 types of classroom behaviors and their classification criteria. By clipping and frame extraction from surveillance videos, target detection, manual annotation, temporal association, and other operations, a multi-label behavior dataset was created. This article also proposes a Weakly supervised fine-grained classification SlowFast SlowFast behavior recognition algorithm, which improves the accuracy of recognizing small difference classroom behaviors from an intra-class classification perspective. By using attention-guided local feature enhancement in the path, weakly supervised fine-grained classification of behavior target local features was achieved. Experimental results showed the algorithm improves behavior recognition accuracy by 4%-11% for specific behaviors and 5.75% overall, contributing to teaching quality evaluation systems.